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At least 469 records · Page 26

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DOE OSTI2020

Validation of FAST.Farm Against Full-Scale Turbine SCADA Data for a Small Wind Farm

FAST.Farm is a new midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on some of the principles of the Dynamic Wake Meandering model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous Dynamic Wake Meandering (DWM) implementations. Previous FAST.Farm verification studies show the similarities and differences between FAST.Farm and large-eddy simulations for rigid and flexible turbines. In this validation study, FAST.Farm turbine responses are compared to multiturbine measurements from a subset of a full-scale wind farm. FAST.Farm predictions of turbine generator power, rotor speed, and blade pitch for five-turbine simulations are compared to supervisory control and data acquisition results. Results reveal that FAST.Farm generator power mean and standard deviation results reasonably match measured data for upstream and downstream turbines, as well as the mean rotor speed and blade pitch above rated wind speeds. However, FAST.Farm generally underpredicts the mean rotor speed and overpredicts the mean blade pitch below rated operation. These errors are likely related to inaccuracies in the generic controller simulated. Despite controller differences, FAST.Farm predicts the same overall relative rotor power trends for all waked turbines at all wind speeds.

17 WIND ENERGY↗

DOE OSTI2025

Simultaneous Control of Unburned NH 3 and NO x Emissions From High Load Dual-Fuel Ammonia Operation on a High-Speed Diesel Engine Using a Cu-SCR System

Dual-fuel ammonia strategies are being investigated as a promising way to utilize NH 3 as an alternative fuel for internal combustion engines in the maritime sector. One of the remaining barriers to implementing dual-fuel NH 3 combustion strategies is understanding ways to minimize unburned NH 3 and nitrogen oxide (NO x ) emissions from these engines, both of which are elevated relative to a conventional diesel baseline. Selective catalytic reduction (SCR) systems are widely used for lean NO x emission controls for engines across transportation and stationary energy applications. SCR systems use a reducing agent, such as NH 3 , to react with NO x in the exhaust, converting it into nitrogen and water. Typically, NH 3 is injected into the exhaust as a urea solution. In dual-fuel NH 3 engines, where unburned NH 3 is present in the exhaust, an SCR system could be used to mitigate both NH 3 and NO x emissions. The presented work evaluates a commercial copper-zeolite SCR and ammonia slip catalyst system, designed for on-road diesel engine applications, for controlling unburned NH 3 and NO x emissions from a dual-fuel NH 3 combustion engine. The aftertreatment system was installed downstream of a single-cylinder four-stroke diesel engine that has been modified for dual-fuel ammonia use. Furthermore, the emissions were characterized by using a Fourier transform infrared spectrometer for both late- and early-injection diesel pilot strategies over three air–fuel equivalence ratios spanning from 1.6 to 1.0 at 1200 rpm and 12.6 bar IMEP g condition (with greater than 95% ammonia energy fraction). Initial findings indicate that the SCR achieves more than 99% NO x conversion with less than 50 ppm NH 3 slip at air–fuel equivalence ratios greater than 1.4 at the operating conditions investigated. However, these benefits are accompanied by additional N 2 O emissions that are formed over the Cu-SCR.

Catalysts↗

DOE OSTI2020

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This paper introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. In conclusion, the architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

42 ENGINEERING↗

DOE OSTI2023

Stochastic Framework for Optimal Control of Planetary Reentry Trajectories Under Multilevel Uncertainties

We present a novel stochastic optimal control framework that accounts for various types of uncertainties, with application to reentry trajectory planning. The formulation of the optimal trajectory control problem is presented in the context of an indirect method where a functional objective associated with the terminal vehicle speed is to be minimized. Uncertain input parameters in the optimal trajectory control model, including aerodynamic parameters and initial and terminal conditions, are modeled as aleatory random variables, while the statistical parameters of these aleatory distributions are themselves random variables. The parametric and model uncertainties are simultaneously propagated through an extended polynomial chaos expansion (EPCE) formalism. Several metrics are described to evaluate response statistics and presented as insightful tools for robust decision making. Specifically, the response probability density function (PDF) reflecting influence of both epistemic and aleatory uncertainties is obtained. By sampling over the random variables representing model error, an ensemble of response PDFs is generated and the associated failure probability is estimated as a random variable with its own polynomial chaos expansion. Besides, the sensitivity index functions of response PDF with respect to the statistical parameters are evaluated. Coupling parametric and model uncertainties within the EPCE framework leads to a robust and efficient paradigm for multilevel uncertainty propagation and PDF characterization in general optimal control problems.

Engineering↗

DOE OSTI2017

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Control and Analysis for a Self-Excited Induction Generator for Wind Turbine and Electrolyzer Applications

Self-Excited Induction Generation(SEIG) is very rugged, simple, lightweight, and it is easy and inexpensive to implement, very simple to control, and requires a very little maintenance. In this variable-speed operation, the SEIG needs a power electronics interface to convert from the variable frequency output voltage of the generator to a DC output voltage for battery or other DC applications. In our study, a SEIG is connected to the power electronics interface such as diode rectifier and DC/DC converter and then an electrolyzer is connected as a final DC load for fuel cell applications. An equivalent circuit model for an electrolyzer is utilized for our application. The control and analysis for the proposed system is carried out by using PSCAD and MATLAB software. This study would be useful for designing and control analysis of power interface circuits for SEIG for a variable speed wind turbine generation with fuel cell applications before the actual implementation.

17 WIND ENERGY↗

DOE OSTI2024

Developing and Evaluating a Smart Curtailment Strategy Integrated with a Wind Turbine Manufacturer Platform

The Renewable Energy Wildlife Institute lead a team of scientists, wind developers, and turbine manufacturers in a study to develop and test a “smart curtailment” system intended to help reduce bat collisions with wind turbines. The Vestas Bat Protection System (VBPS) is a newly developed software module within the Supervisory Control and Data Acquisition (SCADA) system of Vestas turbines. The VBPS combines data from commercially available environmental sensors and the turbine’s built-in sensors with the Vestas SCADA system. VBPS is designed to receive environmental data from sensors on the turbine such as temperature, wind speed, wind direction, time of day, and time of year, relays that information to the SCADA system to determine whether to execute turbine curtailments at any given time. The goals of this study were to 1) develop a bat fatality risk model based on bat activity data and environmental data collected in year 1, and to 2) evaluate the VBPS, using the bat fatality risk model to implement curtailment, in comparison to “blanket curtailment” (turbines curtailed when wind speed is below 5.0 meters per second (m/s)) and “control” (normally operating, feathered below 3.0 m/s) turbines in year 2. The field study took place at a wind energy facility in Iowa during the fall bat migration seasons (July – October) in 2021 and 2022. For VBPS to succeed as a viable strategy for the minimization of bat fatalities, it should meet or exceed the performance of blanket curtailment. Specifically, the VBPS should meet the following performance targets to demonstrate whether it an effective, practical risk reduction measure: (1) Turbines operating VBPS should have equal or fewer bat fatalities compared to turbines operating with blanket curtailment, and significantly fewer bat fatalities compared to control turbines; and (2) Turbines operating VBPS should have greater power production compared to turbines operating with blanket curtailment. The study was completed in accordance with the Statement of Project Objectives and within the terms of the Budget Justification. This Final Report describes the progress, challenges, and outcomes of the study.

17 WIND ENERGY↗

DOE OSTI2018

Fast Numerical Solution of the Plasma Response Matrix for Real-time Ideal MHD Control

To help effectuate near real-time feedback control of ideal MHD instabilities in tokamak geometries, a parallelized version of A.H. Glasser’s DCON (Direct Criterion of Newcomb) code is developed. To motivate the numerical implementation, we first solve DCON’s δW formulation with a Hamilton-Jacobi theory, elucidating analytical and numerical features of the ideal MHD stability problem. The plasma response matrix is demonstrated to be the solution of an ideal MHD Riccati equation. We then describe our adaptation of DCON with numerical methods natural to solutions of the Riccati equation, parallelizing it to enable its operation in near real-time. We replace DCON’s serial integration of perturbed modes—which satisfy a singular Euler- Lagrange equation—with a domain-decomposed integration of state transition matrices. Output is shown to match results from DCON with high accuracy, and with computation time < 1s. Such computational speed may enable active feedback ideal MHD stability control, especially in plasmas whose ideal MHD equilibria evolve with inductive timescale $\tau$ ≳ 1s—as in ITER. Further potential applications of this theory are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Underwater characterization of control rods for waste disposal using SMOPY

Storage of spent fuel assemblies in cooling ponds requires careful control of the geometry and proximity of adjacent assemblies. Measurement of the fuel burnup makes it possible to optimise the storage arrangement of assemblies taking into account the effect of the burnup on the criticality safety margins ('burnup credit'). Canberra has developed a measurement system for underwater measurement of spent fuel assemblies. This system, known as 'SMOPY', performs burnup measurements based on gamma spectroscopy (collimated CZT detector) and neutron counting (fission chamber). The SMOPY system offers a robust and waterproof detection system as well as the needed capability of performing radiometric measurements in the harsh high dose - rate environments of the cooling ponds. The gamma spectroscopy functionality allows powerful characterization measurements to be performed, in addition to burnup measurement. Canberra has recently performed waste characterisation measurements at a Nuclear Power Plant. Waste activity assessment is important to control costs and risks of shipment and storage, to ensure that the activity level remains in the range allowed by the facility, and to declare activity data to authorities. This paper describes the methodology used for the SMOPY measurements and some preliminary results of a radiological characterisation of AIC control rods. After describing the features and normal operation of the SMOPY system, we describe the approach used for establishing an optimum control rod geometric scanning approach (optimum count time and speed) and the method of the gamma spectrometry measurements as well as neutron check measurements used to verify the absence of neutron sources in the waste. We discuss the results obtained including {sup 60}Co, {sup 110m}Ag and {sup 108m}Ag activity profiles (along the length of the control rods) and neutron results including Total Measurement Uncertainty evaluations. Full self-consistency checks were performed and these demonstrate the validity of the techniques. The results are described and analysed in the context of the measurement performance of the equipment. Different casks were fully characterized using a 60 mm{sup 3} CZT detector, to determine the total activities and spatial profiles. A total activity range measurement of 1x10{sup 8} - 1x10{sup 13} Bq/cm was found to be achievable. Finally, comments are made, based on our measurements, on the ability of this equipment for performing in-situ characterisation of wastes in the harsh environments typical of fuel assembly and waste storage ponds and silos. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗

DOE OSTI2015

Retrofit device to improve vapor compression cooling system performance by dynamic blower speed modulation

A device and method are provided to improve performance of a vapor compression system using a retrofittable control board to start up the vapor compression system with the evaporator blower initially set to a high speed. A baseline evaporator operating temperature with the evaporator blower operating at the high speed is recorded, and then the device detects if a predetermined acceptable change in evaporator temperature has occurred. The evaporator blower speed is reduced from the initially set high speed as long as there is only a negligible change in the measured evaporator temperature and therefore a negligible difference in the compressor's power consumption so as to obtain a net increase in the Coefficient of Performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Unitary Thermal Energy Management for Propulsion Range Augmentation (UTEMPRA)

Battery Electric Vehicles (BEVs) encounter weather-dependent driving range. A major drain on the vehicle battery is the HVAC system which, when typically using resistive heating only, can result in a reduction of up to 50% of the driving range in winter weather. In this project, a BEV thermal management system is designed and demonstrated on a Fiat 500e BEV. The system comprises a simple, compact, semi-hermetic refrigerant sub-system and an accompanying a coolant circulatory system which, through an innovative valve arrangement, is used to transfer heat between the several heat sources and sinks in the BEV. The system is capable of A/C and heat pump operation, thermal management of the traction battery and powertrain electronics and also recycles waste heat of the vehicle powertrain to heat the cabin to further converse energy. An automated control system is implemented and tested that selects the relevant mode of operation and optimizes energy usage while meeting vehicle thermal requirements. In wind tunnel tests, the vehicle and UTEMPRA system demonstrated 13% (the target of the project was set at 15%) range increase at -10°C driving at a steady 40 kph speed. The vehicle also demonstrated full capability to automatically control thermal conditions at a wide range of ambient temperatures thus demonstrating the viability of such a system in practice.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Amini, Mohammad Reza↗

DOE OSTI2019

Dry Cask Shipments of ATR Spent Nuclear Fuel at INL

The High Load Charger (HLC) cask will be replacing the Advanced Test Reactor (ATR) spent fuel transfer cask that has been used at Idaho National Laboratory (INL) to wet-transfer spent ATR fuel elements from ATR to Idaho Nuclear Technology and Engineering Center (INTEC) in order to support future plans at both facilities. INTEC intends to discontinue use of the CPP-666 storage pools in compliance with the Idaho Settlement Agreement and the basins will no longer be available to store spent ATR fuel elements. In order to support the core-internals change out in 2021 for ATR, spent ATR fuel elements must be removed from the canal and shipped to INTEC. INL contractors will use the HLC cask for safe, dry transfer of spent Advanced Test Reactor (ATR) fuel elements between the ATR Complex managed by Battelle Energy Alliance, LLC and the Idaho Nuclear Technology and Engineering Center (INTEC) managed by Fluor Idaho, LLC. The HLC cask (also known as the High Load Charging Cask or High Load Charger) is a lead-shielded, stainless steel transfer container originally designed in 1954 by the Idaho Nuclear Corporation to meet the radiation and contamination controls requirements of the time for shipment of spent nuclear fuel. The cask has been modified over the years and is currently used for wet-to-dry transfers at INTEC. It is approved only for out-of-commerce transfers. The cask will arrive at the ATR where eight spent ATR fuel elements will be loaded into two, newly-designed buckets. Each bucket is a quarter section, right-cylinder shape that has four compartments. The cask will be lifted out of the ATR canal allowing most of the water to drain from the cask. Processing the 12,500 kg cask will be completed in ATR and then transported on a tractor trailer to INTEC where the loaded buckets will be removed from the cask. The loaded buckets will undergo a drying process before placing the four buckets into a canister for long-term storage in the Irradiated Fuel Storage Facility (IFSF) at INTEC. A new transportation plan that was submitted to, and approved by, the U.S. Department of Energy to provide the cask safety basis, which describes the hazard analysis and accident analysis for the cask during transport. Supporting accident analysis include criticality, thermal, dose, and drop analyses. Of particular interest for this cask was the drop analysis from which was derived the speed limit for the transport. The drop analysis addressed drops in air onto an unyielding surface and in water onto an energy-absorbing pad for the cask in the ATR facility. These analyses are typically applied as the bounding drop scenarios during transport; however, this resulted in a safety basis speed limit of 12.9 kph. In order to more accurately represent the in-transit accident scenario, LS-DYNA software was procured and the model was updated to more closely represent the potential drop heights and impact surfaces of asphalt and soil for the cask while in transit. This analysis resulted in a safety basis speed limit of 48 kph. This safety basis speed limit is more practical to implement and control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Dry Cask Shipments of ATR Spent Nuclear Fuel at INL - 19600

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Control of SiC Based Integrated DC-DC Powertrain Charger for Electric Vehicles

In this paper, an automated controller for an integrated, reconfigurable dc-dc converter for plugin electric vehicles (EVs) is presented. The integrated converter combines both an interleaved boost converter and dual active bridge (DAB) converter, sharing magnetics and switching devices. The converter switches between operating modes using the existing battery management system (BMS) contactors. The integrated converter operates in isolated DAB mode during battery charging operation. During traction operation, the integrated converter is operated in interleaved boost mode for heavy load and DAB mode during light load, high voltage operation. An online transition algorithm is used to transition from interleaved boost mode to DAB mode with minimal bus distortion. The automated controller selects the operating mode and inverter bus voltage at different torque-speed conditions to get maximum overall traction drive efficiency. An experimental prototype of the integrated converter demonstrates the automated controller functionality.

Anwar, Saeed↗

DOE OSTI2020

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2014

Poster — Thur Eve — 56: Design of Quality Assurance Methodology for VMAT system on Agility System equipped with CVDR

The aim of this study was to analyze the feasibility of designing comprehensive QA plans using iComCAT for Elekta machines equipped with Agility multileaf collimator and continuously variable dose rate. Test plans with varying MLC speed, gantry speed, and dose rate were created and delivered in a controlled manner. A strip test was designed with three 1 cm MLC positions and delivered using dynamic, StepNShoot and VMAT techniques. Plans were also designed to test error in MLC position with various gantry speeds and various MLC speeds. The delivery fluence was captured using the electronic portal-imaging device. Gantry speed was found to be within tolerance as per the Canadian standards. MLC positioning errors at higher MLC speed with gravity effects does add more than 2 mm discrepancy. More tests need to be performed to evaluate MLC performance using independent measurement systems. The treatment planning system with end-to-end testing necessary for commissioning was also investigated and found to have >95% passing rates within 3%/3mm gamma criteria. Future studies involve performing off-axis gantry starshot pattern and repeating the tests on three matched Elekta linear accelerators.

07 ISOTOPE AND RADIATION SOURCES↗